Escherichia coli serves as an excellent model for the study of fundamental cellular processes such as metabolism, signalling and gene expression. Understanding the function and organization of proteins within these processes is an important step towards a ‘systems’ view of E. coli. Integrating experimental and computational interaction data, we present a reliable network of 3,989 functional interactions between 1,941 E. coli proteins (∼45% of its proteome). These were combined with a recently generated set of 3,888 high-quality physical interactions between 918 proteins and clustered to reveal 316 discrete modules. In addition to known protein complexes (e.g., RNA and DNA polymerases), we identified modules that represent biochemical pathways (e.g., nitrate regulation and cell wall biosynthesis) as well as batteries of functionally and evolutionarily related processes. To aid the interpretation of modular relationships, several case examples are presented, including both well characterized and novel biochemical systems. Together these data provide a global view of the modular organization of the E. coli proteome and yield unique insights into structural and evolutionary relationships in bacterial networks.
Genes and their protein products do not operate in isolation, but form components of highly interconnected biological systems. Identifying the connections between components is therefore critical to understanding how these processes are organized and operate. E. coli is the leading model bacterium; however despite its importance in biological and medical discovery, an accurate atlas of these interactions is still lacking. On the other hand, several computational and experimental procedures have been applied on a high-throughput basis to provide collections of interaction data of varying quality and coverage. Using a sophisticated mathematical framework, we have combined and benchmarked these data to create a single, highly reliable set of interactions that encompasses almost 50% of the E. coli proteome. Organizing these data on the basis of their interactions, we identify groups of proteins representing functionally coordinated modules such as molecular machines (e.g., the flagellum) and biochemical pathways. Finally through examining the organization of E. coli interactions in the context of evolution, we propose a new model of bacterial network evolution that accounts for the integration of foreign genes acquired through horizontal gene transfer mechanisms.